1. Technical Field
One or more embodiments of the present disclosure relate generally to shape identification in an image. More specifically, one or more embodiments of the present disclosure relate to systems and methods for identifying shapes in an image by using Bézier curves.
2. Background and Relevant Art
Computing devices are useful in identifying various shapes and features in images. For example, a user may use a computing device to detect faces, animals, or other objects in an image. One popular method of performing image recognition is the Hough transform, which uses a feature extraction technique to find objects in an image. Conventional systems and methods, including the Hough transform, however, perform image recognition by comparing pixels in an image to pixels of a known object. These conventional systems and methods require intensive processing power and large amounts of computer resources.
To illustrate, an image may include a circle. To identify the circle, conventional systems and methods detect every pixel belonging to an unknown object (e.g., the circle). In many circumstances, the conventional systems and methods create arrays and/or matrices for each detected edge pixel (i.e., pixels that form the outline of the unknown object). For example, for each detected edge pixel, the conventional systems and methods perform a series of algorithms to identify characteristics of the edge pixels, such as the angle and distance of the edge pixel. Then, using the calculated information, the conventional systems and methods try to identify the unknown object by comparing the edge pixel to pixels of known shapes until the conventional systems and methods identify a shape that matches the unknown object.
Consequently, conventional systems and methods require a large computational cost, which often results in a user waiting longer periods for a computing device to detect the shape of the unknown object. Further, unknown objects that are larger in size or have a larger number of pixels require even greater amounts of computational cost to detect a known shape that matches the unknown objects. Likewise, under the conventional systems and methods, images that include multiple unknown objects further increase the computational expense needed to detect each unknown object.
Furthermore, even without considering the computational expense, many conventional systems and methods still do not produce accurate shape matches for unknown objects. Because conventional systems perform pixel comparisons, an object that is a different size, rotated, or skewed may not be correctly matched or recognized. For example, a circle that is slightly skewed may be incorrectly matched to an oval, or may result in a conventional system returning a result of no matching known shape. Likewise, an enlarged image of a known object may not be correctly matched to the known object due to its enlarged nature in the image.
Thus, these and other problems exist with regard to image recognition, and in particular, shape recognition in the field of computer vision.